AI-driven maternal and child healthcare nursing education: Network analysis of self-efficacy and usage demands

BACKGROUND: The application of artificial intelligence (AI) in maternal and child health nursing education is increasingly widespread, yet the dynamic relationship between nurses' AI self-efficacy and usage demands remains underexplored. In China's maternal and child health sector, nurses face high work pressure and training shortages, hindering AI integration. This study uses network analysis to uncover the complex structure of AI self-efficacy and demands among Chinese nurses, informing optimized AI training strategies. METHODS: A cross-sectional study employed convenience sampling of registered nurses (N = 848) from mainland China's maternal and child health institutions (January 1-March 1, 2025). The AI Self-Efficacy Scale (AISES; 22 items, 4 dimensions: assistance, anthropomorphic interaction, comfort, technical skills) assessed self-efficacy, with added questions on AI usage and training needs. LASSO-regularized partial correlation networks were built using R (qgraph package), characterizing key nodes via strength centrality, bridge strength, and predictability. Bootstrap methods verified network stability and edge accuracy. RESULTS: = 0.905). Key bridge: AI_1 ("AI interaction vivid"; bridge strength = 3.403). Associate-degree nurses (N = 189) showed higher centrality in technical skills (TS_4, "AI jargon clear"; Δ = 0.429) and comfort (CF_5, "AI interaction relaxed"; Δ = 0.148) versus bachelor's-or-higher (N = 659). Only 13.7 % received AI training; 43.6 % had no exposure, underscoring deficiencies. CONCLUSIONS: Network analysis highlights anthropomorphic interaction and learning assistance as core in AI self-efficacy, offering targets for targeted training. Suggestions include anthropomorphic training, AI resource platforms, terminology courses, low-stress exercises, and case studies to enhance AI integration, nursing quality, and maternal-infant outcomes. Cross-sectional limitations necessitate future longitudinal studies to validate effects and address grassroots needs.